# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from tests.mark_utils import arg_mark

import numpy as np
import pytest

import mindspore as ms
import mindspore.nn as nn
import mindspore.ops as ops


class Net(nn.Cell):
    def construct(self, x):
        return ops.sgn(x)


@arg_mark(plat_marks=['platform_gpu', 'cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level2',
          card_mark='onecard', essential_mark='unessential')
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_sgn_normal(mode):
    """
    Feature: sgn
    Description: Verify the result of sgn
    Expectation: success
    """
    ms.set_context(mode=mode)
    net = Net()
    x = ms.Tensor([[3 + 4j, 7 - 24j, 0, 6 + 8j, 8], [15 + 20j, 7 - 24j, 0, 3 + 4j, 20]], dtype=ms.complex64)
    output = net(x)
    expect_output = np.array([[0.6 + 0.8j, 0.28 - 0.96j, 0. + 0.j, 0.6 + 0.8j, 1. + 0.j],
                              [0.6 + 0.8j, 0.28 - 0.96j, 0. + 0.j, 0.6 + 0.8j, 1. + 0.j]])
    print(output)
    print(expect_output)
    assert np.allclose(output.asnumpy(), expect_output)
